Complete Machine Learning Bootcamp [Updated]

Complete Machine Learning Bootcamp [Updated], available at $44.99, has an average rating of 4.45, with 73 lectures, based on 35 reviews, and has 5433 subscribers.

You will learn about Amazon Sagemaker to build, train, and deploy machine learning models at scale Amazon Comprehend for natural Language processing and text analytics Amazon Lex for conversational interfaces for your applications powered by the same deep learning technologies as Alexa Amazon Polly to turn text into lifelike speech using deep learning Object and scene detection,Image moderation,Facial analysis,Celebrity recognition,Face comparison,Text in image and many more Amazon Transcribe for automatic speech recognition Amazon Translate for natural and accurate language translation Echo Bot Facebook Chat bot Question and Answer Maker LUIS(Language Understanding) Text Analytics Detecting Language Analyze image and video Recognition handwritten from text Generate Thumbnail Content Moderator Custom Vision Translate Simple chatbot integrate in HTML websites This course is ideal for individuals who are Anyone who is curious to experiment with Artificial Intelligence and develop applications or simply interested in exploring AWS Machine Learning and its powerful services available on AWS Cloud. or Basic knowledge of web development or Minimal level of Azure Services It is particularly useful for Anyone who is curious to experiment with Artificial Intelligence and develop applications or simply interested in exploring AWS Machine Learning and its powerful services available on AWS Cloud. or Basic knowledge of web development or Minimal level of Azure Services.

Enroll now: Complete Machine Learning Bootcamp [Updated]

Summary

Title: Complete Machine Learning Bootcamp [Updated]

Price: $44.99

Average Rating: 4.45

Number of Lectures: 73

Number of Published Lectures: 73

Number of Curriculum Items: 73

Number of Published Curriculum Objects: 73

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Amazon Sagemaker to build, train, and deploy machine learning models at scale
Amazon Comprehend for natural Language processing and text analytics
Amazon Lex for conversational interfaces for your applications powered by the same deep learning technologies as Alexa
Amazon Polly to turn text into lifelike speech using deep learning
Object and scene detection,Image moderation,Facial analysis,Celebrity recognition,Face comparison,Text in image and many more
Amazon Transcribe for automatic speech recognition
Amazon Translate for natural and accurate language translation
Echo Bot
Facebook Chat bot
Question and Answer Maker
LUIS(Language Understanding)
Text Analytics
Detecting Language
Analyze image and video
Recognition handwritten from text
Generate Thumbnail
Content Moderator
Custom Vision
Translate
Simple chatbot integrate in HTML websites

Who Should Attend

Anyone who is curious to experiment with Artificial Intelligence and develop applications or simply interested in exploring AWS Machine Learning and its powerful services available on AWS Cloud.
Basic knowledge of web development
Minimal level of Azure Services

Target Audiences

Anyone who is curious to experiment with Artificial Intelligence and develop applications or simply interested in exploring AWS Machine Learning and its powerful services available on AWS Cloud.
Basic knowledge of web development
Minimal level of Azure Services

Do you know which job is the highest paying and most in demand job? If you look for the answer whether on Google or any job listing search engine. Then the answer will remain same for the question, i.e. Machine Learning Engineer.

If you are wondering where to begin this journey of learning, then this course is for YOU. In this course you will learn and practice all the services related to Machine Learning in AWS and Microsoft Azure Cloud. This course provides you in depth learning about cognitive computing, machine learning as well as cloud computing. It offers 5 hrs learning with practical hands on ML and cognitive services

This learning path has been designed to help people get a deeper understanding of the real-life problems in the field.

You will be able to integrate these services into your Web, Android, IoT, Desktop Applications like Face Detection, ChatBot, Voice Detection, Text to custom Speech (with pitch, emotions, etc), Speech to text, Sentimental Analysis on Social media or any textual data.

AWS Machine Learning Services are-

Amazon Sagemaker to build, train, and deploy machine learning models at scale

Amazon Comprehend for natural Language processing and text analytics

Amazon Lex for conversational interfaces for your applications powered by the same deep learning technologies as Alexa

Amazon Polly to turn text into lifelike speech using deep learning

Object and scene detection,Image moderation,Facial analysis,Celebrity recognition,Face comparison,Text in image and many more

Amazon Transcribe for automatic speech recognition

Amazon Translate for natural and accurate language translation

Microsoft Azure Machine Learning Services are-

Text Analytics
Detecting Language
Analyze image and video
Recognition handwritten from text
Generate Thumbnail
Content Moderator
Translate and many more things

ALL THE BEST !!

Course Curriculum

Chapter 1: AZURE Machine Learning Overview

Lecture 1: Introduction to Azure Machine Learning

Lecture 2: What are Cognitive services? [Summary]

Lecture 3: Difference between AI and Machine Learning [Summary]

Chapter 2: NLP Project

Lecture 1: Build frontend for ML Application

Lecture 2: Build Backend for ML Application

Lecture 3: Add NLP task (translation)

Lecture 4: Demo: Translation ML app

Lecture 5: Creating Sentiment Analysis ML app

Lecture 6: Demo: Sentiment Analysis ML app

Chapter 3: Quick Recap

Lecture 1: Quick Recap – NUMPY

Lecture 2: Quick Recap – Pandas

Chapter 4: Computer vision

Lecture 1: Computer Vision : Create Service

Lecture 2: Computer Vision : Analyze image , text in image and generate thumbnail

Chapter 5: Content Moderator

Lecture 1: Content Moderator:Text Moderation

Lecture 2: Content Moderator:Image and Video Moderation

Lecture 3: Content Moderator : Explore Dashboard

Lecture 4: Content Moderator : Create Instance

Chapter 6: Custom Vision

Lecture 1: Custom Vision : Create project

Lecture 2: Custom Vision : Upload Image and add tags

Lecture 3: Custom Vision : Train model and predict unlabeled image

Lecture 4: Custom Vision : Delete Resource

Chapter 7: Text Analytics

Lecture 1: Text Analytics :Create Service

Lecture 2: Text Analytics :Language Detection

Lecture 3: Text Analytics :Sentiment Analysis

Lecture 4: Text Analytics :Key Phrase Extraction

Lecture 5: Text Analytics :Entity Recognition

Chapter 8: Translate

Lecture 1: Translate :Creating Service

Lecture 2: Translate :Deploy

Chapter 9: AWS Machine Learning

Lecture 1: Introduction to AWS Machine Learning

Lecture 2: AI services [No machine learning skill]

Chapter 10: Amazon Comprehend

Lecture 1: Amazon Comprehend

Lecture 2: Practical:Amazon Comprehend

Lecture 3: (PythonBoto3)Comprehend 1

Lecture 4: (PythonBoto3)Comprehend 2

Lecture 5: (PythonBoto3)Comprehend 3

Chapter 11: Amazon Lex and Amazon Polly

Lecture 1: Amazon Lex

Lecture 2: Amazon Lex Part-2

Lecture 3: Amazon Polly

Lecture 4: Practical:Chatbot using Amazon Lex

Lecture 5: Practical:Amazon Polly

Lecture 6: (PythonBoto3)Polly

Chapter 12: Amazon Rekognition

Lecture 1: Amazon Rekognition

Lecture 2: Object and scene detection(Overview)

Lecture 3: Object and scene detection(Practical)

Lecture 4: (PythonBoto3)Detect Label

Lecture 5: Facial analysis(Overview)

Lecture 6: Facial analysis(Practical)

Lecture 7: (PythonBoto3)Detect Face

Lecture 8: Celebrity recognition(Overview)

Lecture 9: (PythonBoto3)Celebrity Recognition

Lecture 10: Celebrity recognition(Practical)

Lecture 11: Face comparison(Practical)

Lecture 12: Face comparison(Overview)

Lecture 13: (PythonBoto3)Face Comparison

Lecture 14: Text in image(Overview)

Lecture 15: Text in image(Practical)

Lecture 16: (PythonBoto3)Detect Text in Image

Lecture 17: Visual Analysis

Chapter 13: Amazon SageMaker and AWS DeepLens

Lecture 1: Amazon SageMaker

Lecture 2: AWS DeepLens

Chapter 14: Machine Learning (Using Amazon ML to Predict Responses to a Marketing Offer)

Lecture 1: ML 1 -Prepare Your Data

Lecture 2: ML 2-Create a Training Datasource

Lecture 3: ML 3-Create an ML Model

Lecture 4: ML 4-Use the ML Model to Generate Predictions

Lecture 5: ML 5-Clean Up

Chapter 15: Amazon Transcribe and Translate

Lecture 1: Amazon Transcribe

Lecture 2: Amazon Translate

Lecture 3: Practical:Amazon Transcribe

Lecture 4: (PythonBoto3)Transcribe

Lecture 5: Practical:Amazon Translate

Lecture 6: (PythonBoto3)Translate

Chapter 16: AWS Certified Machine Learning – Specialty (MLS-C01) Exam Guide

Lecture 1: Exam Content

Chapter 17: Bonus Section

Lecture 1: Bonus Lecture

Instructors

Pranjal Srivastava
Docker | Kubernetes | AWS | Azure | ML | Linux | Python

Rating Distribution

1 stars: 1 votes
2 stars: 1 votes
3 stars: 3 votes
4 stars: 8 votes
5 stars: 22 votes

Frequently Asked Questions

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